Regularized approximate policy iteration using kernel for on-line reinforcement learning
By using Reinforcement Learning (RL), an autonomous agent interacting with the environment can learn how to take adequate actions for every situation in order to optimally achieve its own goal. RL provides a general methodology able to solve uncertain and complex decision problems which may be prese...
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| Format: | Doctoral Thesis |
| Language: | English |
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Universitat Politècnica de Catalunya
2015
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| Online Access: | http://hdl.handle.net/10803/308503 |